Posted on October 8, 2025 by DForD Software
So, you're ready to let AI supercharge your software localization? Smart move. Using Large Language Models (LLMs) can save you a ton of time and money, but you can't just blindly throw your text at an AI and hope for the best. To really get the goods, you need a strategy. Here are our top best practices for building a world-class, AI-powered internationalization (i18n) workflow.
Before you even whisper the word "translation," you need to have all your strings in one place. Shoving all your user-facing text into centralized resource files (like `.json` or `.xml`) is i18n 101, but it's absolutely non-negotiable when you're bringing AI into the mix. This gives you a single, clean source of truth that your automated workflow can rely on.
"Think of your string files as the clean, organized kitchen you need before you can start cooking with AI."
LLMs are incredibly smart, but they can't read your mind. To get translations that aren't just literal, but actually *good*, you have to give them context. We're talking about:
This is why we built Quicki18n—to make providing this kind of context dead simple and a natural part of the development process.
AI is a fantastic starting point, but it's not perfect. You absolutely need a human review process. Whether it's a professional translator or a native-speaking team member, you need a real person to catch the subtle cultural nuances and tricky grammar that an AI might miss. Think of the AI as a brilliant junior translator who prepares the first draft, and your human reviewer as the seasoned pro who gives it the final polish.
If you're working in a field with a lot of jargon (hello, medical and financial tech!), a generic AI might struggle. The solution? Fine-tune it. By training a general-purpose LLM on your own company's specific glossaries and past translations, you can create a model that speaks your language fluently, right from the get-go.
This is where you get the real bang for your buck. The magic of AI-powered i18n is in the automation. You should be integrating this process directly into your CI/CD pipeline. Imagine a world where every time a developer merges new text, it's automatically sent for translation, and the results come back as a new pull request, ready for review. That's not science fiction; that's a modern, continuous localization workflow.
By weaving these practices into your development cycle, you can build an i18n process that's fast, efficient, and delivers amazing results. You get the speed of AI and the quality of human expertise. And tools like Quicki18n are designed to make it all happen.
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